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Seismic vulnerability assessment of urban buildings is among the most crucial procedures to post-disaster response and recovery of infrastructure systems. The present study proceeds to estimate the seismic vulnerability of urban buildings and proposes a new framework training on the two objectives. First, a comprehensive interpretation of the effective parameters of this phenomenon including physical and human factors is done. Second, the Rough Set theory is used to reduce the integration uncertainties, as there are numerous quantitative and qualitative data. Both objectives were conducted on seven distinct earthquake scenarios with different intensities based on distance from the fault line and the epicenter. The proposed method was implemented by measuring seismic vulnerability for the seven specified seismic scenarios. The final results indicated that among the entire studied buildings, 71.5% were highly vulnerable as concerning the highest earthquake scenario (intensity=7MM and acceleration calculated based on the epicenter), while in the lowest earthquake scenario (intensity=5MM), the percentage of vulnerable buildings decreased to approximately 57%. Also, the findings proved that the distance from the fault line rather than the earthquake center (epicenter) has a significant effect on the seismic vulnerability of urban buildings. The model was evaluated by comparing the results with the weighted linear combination (WLC) method. The accuracy of the proposed model was substantiated according to evaluation reports. Vulnerability assessment based on the distance from the epicenter and its comparison with the distance from the fault shows significant reliable results.

Purpose Housing price is a barometer of a national economy. In recent years, Iran experienced high inflation in its economy, which affects everything, including housing. The purpose of this study is the estimation of the value of residential apartments of Tehran using ordinary least square (OLS) and geographically weighted regression (GWR) methods. Design/methodology/approach This paper proposed a method for determining the compound variables and used them to estimate and evaluate the prices in the district six of Tehran city. Also, this paper compared the GWR and OLS methods with different types of factors and their influences in house price estimations. Findings During the high inflation period of the study period, the age of buildings, inflation, parking, storage room and their locations are the most critical factors that affect the price of apartments in district six of Tehran. Besides, compound variables have the most influence on the prediction of the prices. Research limitations/implications The exact location of the apartments in the study area were unknown. Therefore, the positions are extracted from their addresses. The uncertainty of location forced us to ignore the neighborhood terms in the hedonic method. Practical implications The exact locations of the apartments in the study area were unknown. Therefore, the positions are extracted from their addresses. The uncertainty of location forced us to ignore the neighborhood terms in the hedonic method. Originality/value The originality of the proposed method is that it used a different approach to determine the valid variables of the apartment prices. Also, the evaluation of the method showed that the proposed variables are significantly useful.

The green spaces and urban parks play a critical role to improve the quality of life of citizens. In order to meet the principles of the justice-based city and spatial justice in the distribution of public services, it is necessary to evaluate the accessibility to parks in different locations and pay more attention to the places with a low level of accessibility. This study evaluates the spatial justice or inequalities in accessibility to urban parks in District # 11 of Tehran, Iran using a set of spatial indices and GIS tools. Indices used in this study are Covering, Minimum distance, Average distance, Proximity, Two-step floating catchment area and Gravity-based two-step floating catchment area. The results indicate that the level of accessibility to mini and neighborhood parks are almost similar and below the average level. The slight differences in the results are related to the differences in the assumptions and logics of methods. Moreover, the results show that the Coverage, 2SFCA and GB2SFCA (with inappropriate distance decay coefficient) methods face limitations when the accessibility for the community parks are calculated. For example, the Coverage method does not take into account the area of the park for measuring the accessibility level, in turn, this leads to inaccurate results. Overall, the findings show better accessibility to community parks than mini and neighborhood parks. This implies that the municipalities need to increase the number of local mini and neighborhood parks across the city.

The context-aware is the knowledge that leads to better cognition and recognition of the environment, objects and factors, and the way of communication and interactions between them. As a result, it can have a great impact in providing appropriate solutions to various problems. It is possible to integrate consciousness into relief and rescue discussions and to take steps to improve and make realistic solutions. In this study, this issue was addressed in the earthquake crisis, due to a large number of seismic faults in Iran, is one of the major crises in Iran and many parts of the world. Hence, the contexts of rescuers, teams, and environment as the main textures in the above-mentioned issue are investigated and their relationship with each other and the priorities of activities and locations by identifying specialties and the physical and situational conditions of the relief workers, and an algorithm was designed and optimized to optimize the allocation of the relief workers to the affected areas and the necessary activities. Finally, the improvement of the 2.4 fold results of the algorithm and the proposed structure of this research resulted in the ratio of non-use of this algorithm.

Disability has been one of the most important problems of social communities throughout the ages. As population and urbanization have grown dramatically over recent years, this problem has more and more created the gap between people with disabilities and ordinary people in terms of access to resources, social services and social partnerships. Therefore, this study attempts to demonstrate the ratio of presence of wheelchair users in a community compared to the total population of the same community and evaluate their patterns of presence in different conditions, for example, various weather conditions. For this purpose, we used the You Look Only Once version 3 (YOLOv3) algorithm which is a multilayer deep learning object detection tool to analyze and extract wheelchair users from three different sets of images taken by a camera located in an intersection proximate to a rehabilitation center in Quebec, Canada. The results show that the proportion of wheelchair users in the sample community is 7.4%, while the population with mobility disabilities in Canada is 9.6%.

The idea of a smart city seeks to improve the quality of life, which environmental sustainability is one of its main challenges, and low air quality is an important obstacle to achieving the sustainability. Accordingly, we investigated the relationships between Urmia Lake drying-up and PM10 concentration in Tabriz by 3D visualization of space-time cube in ArcGIS pro 2.7. So, Satellite images of the lake and PM10 data were collected. Next, a space-time cube was created for each season of the lake and PM10 during 2004 to 2019. Then Time series analysis, Emerging hot spot analysis and 3D visualization of these cubes were performed. The results showed that there is a clear relationship between drying-up of the lake and increasing of PM10 in Tabriz and we recognized that the drying-up has a significant impact on increasing of PM10 concentration in Tabriz.

Predictive modelling and its application in land change science has been considerably advanced, however, further performance improvement of the existing models is undergoing. CA-Markov is one of the frequently used methods for this purpose, while a few studies have investigated the possibility of improving it with respect to capturing heterogeneous directional variation of change instead of a global approach. The main objective of this study is to improve the simulation performance of CA-Markov model by developing a direction-based approach so that the predictive changes vary along different directions resulting in more accurate predictions. The proposed approach was tested on Babol city of Iran for three timestamps of 1985, 2001 and 2015. Thereafter, the performance accuracy of the global and direction-based approaches was compared. For doing so, Pearson Chi-square statistic, Shannon's entropy, and degree-of-goodness index were used to check the spatial structure of urban expansion. The findings reveal that our proposed approach outperforms better than the global approach confirming the suitability of our approach for presence and futuristic urban planning. Our conclusions draw attention of the respective stakeholders and decision makers to take our findings into consideration and also take preparedness measures for the speculative consequences.

Many parts of the world may have suitable conditions and potential to establish two or more renewable energy farms. Given the pervasive use of renewable energy globally, assessing the potential of regions to establish a multi-renewable energy farm is of great importance. This study aimed to assess the potential for the establishment of renewable energy farms (solar, wind, biomass, and geothermal) in the eastern regions of Iran. For the first time, the potential for establishing multi-renewable energy farms in an area has been assessed. For this purpose, a series of environmental and economic criteria were addressed and investigated. Respectively, Analytical Network Process (ANP) and Fuzzy logic were employed for obtaining the required weights as well as accounting for the element of uncertainty among the different criteria. The final suitability maps for identification of the most optimal locations for the institution of renewable energy farms were obtained using Weighted Linear Combination (WLC) method. The study area was classified as highly suitable for the establishment of renewable energy farms, as maintained by the final results, wherein 5, 13, 23, and 19% of the entire study area were selected as eligible placements for the institution of biomass, geothermal, solar, and wind power plants, respectively. In consonance with the final map obtained using a combination of individual suitability maps, a total of 5465 km2 worth of area was categorized as highly suitable for the establishment of renewable energy farms. Results also were indicative of the prominence of the different weights assigned to each criterion on identifying the optimal choices of the region concerning the establishment of renewable energy farms. The results can be further used for and are highly advantageous to various managerial, planning, and decision-making procedures in connection with the development of prospective renewable energy sources.

Adequate coverage is an important issue in geosensor networks in order to fulfill the sensing applications in urban areas. GIS as well as Optimization methods are widely used to distribute geosensors in the network to achieve the desired level of coverage. Most of the algorithms applied in urban domain suffer from the lack of considering real environmental information. In this paper, the problem of placing sensors to get optimum coverage is studied by investigating the concept of urban contextual information in sensor network. Then, a local GIS-based context-aware framework of sensor network deployment optimization method is introduced. Obtained results of our algorithm under different working conditions and applications show the effectiveness of our approach.

One of the most important applications of remote sensing is presented in mining and exploration of mineral deposits and evaluation of prospective targets. This project discusses how to use remote sensing knowledge in order to make classification and separation of surface rocks in the Dir-o-Morreh mine. The main purpose of this research is to identify the areas containing high-quality basalt. In this regard, we utilize ASTER multi-spectral satellite imagery, which has relatively good spectral and spatial resolution. At the first step, in order to achieve the correct spectral composition of the basalt spectrum, the spectral signature of basalt stone, defined by Johns Hopkins University, was used. Afterward, the basalt extraction index (BEI) was defined regarding the behavior of the ASTER satellite image bands as well as the initial data provided by the owners of the intended study area. Then, the Convolution and Morphology filter was applied over the images to extract high-quality basalt using an appropriate color composition of the images. At the next step, in order to have better visualization, different maps containing different classes were created using the Maximum Likelihood algorithm. Finally, two indices were developed regarding all research data and field investigations in order to extract basalt zones. The first index discovers basalt zones in the study area, and the second one classifies high-quality basalt and altered basalt zones.

Dust storms are considered as one of the most important environmental challenges in the West Asia region. In addition to the harmful impacts of dust storms on human health, they also have particular effects on socioeconomic and agroecological domains of human communities. Identify the sources of dust storms is the first step to combat against these devastating phenomena. Accordingly, the present study was conducted to determine dust sources of the Tigris and Euphrates basin using satellite and climatic data. Monthly LST and NDVI of MODIS, monthly wind speed, soil moisture, and absolute air humidity data from GLDAS, monthly TRMM precipitation, and soil texture data of FAO were used. The Analytic Hierarchy Process (AHP) model was applied to determine the weights of the collected data (i.e. criteria or drivers for dust storms formation). Susceptible Areas to Dust Storm Formation (SADSF) were determined using the Weighted Linear Combination (WLC) model for months of June, July, and August from 2000 to 2017. After performing SADSF analysis, five main dust sources were identified in the whole basin. To evaluate the accuracy of the results, the number of real Observed Dust Storms (ODS) in each source was compared to the repetition of allocation in SADSF for each pixel over the 18-year period of this study from 2000 to 2017. Results indicated that the area of SADSF has significantly grown for all three months since 2008. The areas of SADSF in June and July were almost the same, while they were significantly bigger than August. Among identified dust sources, the highest SADSF repetition was in the northwest of Iraq followed by eastern Syria, southern Iraq, southeast border of Iraq, and east border of Iraq, respectively. The correlation coefficient between the SADSF repetition with the number of ODS events in those recognized dust sources was equal to 0.88, 0.76, and 0.74 for June, July, and August, respectively, that shows the accuracy of our results in comparison to actual data.